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time series

215 articles · Page 3 of 3
Ctrip Technology
Ctrip Technology
Aug 7, 2018 · Artificial Intelligence

Forecasting and Monitoring in Business Intelligence: Practical Data‑Analysis Methods and Model‑Building Tips

The article explains how a data analyst can use statistical and machine‑learning models such as linear regression, tree‑based boosting, STL decomposition, and Prophet for both non‑time‑series forecasting and time‑series monitoring, highlighting data‑quality concerns, feature‑engineering practices, and deployment considerations like PMML packaging.

BIData AnalysisProphet
0 likes · 13 min read
Forecasting and Monitoring in Business Intelligence: Practical Data‑Analysis Methods and Model‑Building Tips
Efficient Ops
Efficient Ops
Apr 26, 2018 · Operations

How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms

This article explains how 360’s network operations team uses time‑series analysis, statistical thresholds, EWMA, dynamic limits, and machine‑learning models such as K‑Means and Isolation Forest to automatically detect, locate, and remediate traffic anomalies across massive data‑center exits.

AI opsanomaly detectionmachine learning
0 likes · 15 min read
How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms
Efficient Ops
Efficient Ops
Feb 6, 2018 · Operations

Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves

The article recounts the author’s 2017‑onward journey building an intelligent operations platform at Tencent, detailing challenges such as legacy thresholds, AIOps talent shortage, and lack of frameworks, and explains how a two‑stage hybrid unsupervised‑supervised model was devised to automatically detect anomalies across millions of KPI time‑series, enabling scalable root‑cause analysis and cost optimization.

AIOpsOperationsanomaly detection
0 likes · 7 min read
Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves
ITPUB
ITPUB
Jan 16, 2018 · Databases

10 Groundbreaking Database Systems Launched in 2017

A 2017 roundup highlights ten innovative database releases—including a time‑series extension for PostgreSQL, a multi‑model Azure service, Google’s globally distributed Spanner, Amazon’s Neptune graph service, and several open‑source cloud‑native databases—detailing their key features, architectures, and intended use cases.

DatabasesGraphSQL
0 likes · 10 min read
10 Groundbreaking Database Systems Launched in 2017
Java High-Performance Architecture
Java High-Performance Architecture
Oct 13, 2017 · Databases

Why Redis Added Streams and How to Use Them Effectively

Redis introduced the Streams data type to address limitations of sorted sets, lists, and Pub/Sub for handling continuous data flows, offering features like field-value entries, efficient range queries, and client blocking with ID tracking, and the article explains its design, commands, and usage examples.

Data StructuresStreamstime series
0 likes · 7 min read
Why Redis Added Streams and How to Use Them Effectively
Alibaba Cloud Developer
Alibaba Cloud Developer
May 17, 2017 · Databases

How Alibaba Tackles the Massive Challenges of Time‑Series Data Storage

This article details Alibaba's middleware team's exploration of time‑series data characteristics, real‑world monitoring scenarios, the limitations of traditional databases, and the evolution of their custom HiTSDB solution that combines inverted indexing, high‑compression algorithms, and distributed aggregation to meet massive write and query demands.

AlibabaBig DataDatabase
0 likes · 25 min read
How Alibaba Tackles the Massive Challenges of Time‑Series Data Storage
ITPUB
ITPUB
Jun 24, 2016 · Databases

How Ctrip Built a Fast MySQL-Based Time‑Series Storage Engine (CFL)

This article details Ctrip's motivation, design, implementation, performance evaluation, and future plans for a custom MySQL storage engine called CFL that efficiently stores time‑series data by leveraging MySQL's replication and a sequential write‑optimized file format.

CFLMySQLStorage Engine
0 likes · 15 min read
How Ctrip Built a Fast MySQL-Based Time‑Series Storage Engine (CFL)
ITPUB
ITPUB
Dec 3, 2015 · Databases

Choosing the Right Time‑Series Database: Types, Queries, and Performance Trade‑offs

Time‑series data, defined by a timestamp field, appears everywhere, and the article explains how to choose an appropriate time‑series database by comparing two schema models, their query patterns, performance trade‑offs, and why modern solutions like Elasticsearch, columnar stores, and Druid excel at real‑time massive aggregation.

AggregationBig DataDatabase
0 likes · 9 min read
Choosing the Right Time‑Series Database: Types, Queries, and Performance Trade‑offs
21CTO
21CTO
Aug 31, 2015 · Databases

Optimizing Time-Series Storage: Files, LSM Trees, and B‑Tree Strategies

This article examines the evolution and challenges of time‑series storage, compares file‑based, LSM‑tree, and B‑tree approaches, and proposes a vector‑based method to efficiently handle writes, reads, query semantics, dimensions, and aggregation for modern big‑data applications.

AggregationDatabasesLSM
0 likes · 8 min read
Optimizing Time-Series Storage: Files, LSM Trees, and B‑Tree Strategies